Larger deep learning systems can be exhibit qualitative differences from smaller ones. As such, we examine the DIDE phenomenon for SoTA models on ImageNet [1]}, which has \(m_{\text{ImageNet}}\sim 10^6\)  training samples with size \(224\times 224 \times 3\)  (\(=3d\) ).
